Catálogo de publicaciones - libros

For nonlinear dynamic systems, the classical models are not sufficiently accurate, because the parameters are poorly known and are in general time-variants. So, it is important to develop control systems that incorporate learning capabilities in a way that their control systems automatically improve accuracy in real time and become more autonomous. This paper presents different technique used in dynamic nonlinear applications like dynamic test data generation and genetic algorithms. One example is given: an automatic pilot.

This paper presents a fuzzyfication method that consists in determining the parameters of the membership functions by using control error intervals and functions which cross these intervals. The proposed method can use constant or variable control error intervals and linear or nonlinear functions which cross these intervals and which determine the variable parameters of the neuro-fuzzy based nonlinear model with the designed membership functions. First, nonlinear models are presented, then a concrete system is analyzed in both cases: classic and adaptive.

In these experiments we explore the practical implementation of pattern generating electronic circuits. The project takes advantage of fundamental mathematical arguments, based on symmetry, in order to define the circuits and coupling topologies that are used. By constructing networks of low-order dynamical neuron models, and by considering symmetries in the way the neurons are coupled, including time-shift invariant symmetries, the patterns generated by the system are reduced to a predictable few. Using the resulting patterns, the relative phases between the synchronized neurons within the network are used to define gait patterns that are similar to those found in living quadrupeds. Electronic circuits based on these observations are engineered to generate the needed analog signals for driving locomotion in an N-legged robot.

It was in 1896 when S. Arrhenius first noticed the potential effect of human activities on the carbon cycle and the implications for climate change. He put forward the theory that CO in the atmosphere was an important greenhouse gas and that it was a by-product of burning fossil fuels. In 1958, Charles Keeling began the observations at Mauna Loa Observatory, 3650m up a mountain in Hawaii, regarded as far enough away from any carbon dioxide source to be a reliable measuring point. Measurements of CO in the atmosphere have been continuous for almost 50 years. In recent decades, CO increased on average by 1.4 parts per million (ppm) a year because of the amount of fossil fuels burnt.